I used the dredge function with
lme4 package to do a linear model selection with AIC principle. I have about 10 predictors (year, length of whale, 8 different fatty acid concentrations) to test for their effect on the dependent variable (Log transformed PCB concentration in whale blubber).
The best model is very interesting, there is no multicollinearity and the fitted residuals plot looks OK. HOWEVER, the Durbin-Watson test always fails (p < 0.05), meaning our residuals are autocorrelated... Does this invalidate my results and/or is this a problem? I am using model selection as it was advised by my supervisor, but the fatty acids I am including as predictors are proportional data (all adding to one) and tend to violate assumptions of normality and can be highly correlated. I had originally used a multivariate analysis (factor analysis) for my work, but was advised to use model selection to examine important predictors of sumPCB concentrations in whales (individual fatty acids, body size, and year). ... I tried a GLM model to correct for this autocorrelation but the results are different and less interesting, only one significant predictor...
Thank you for your feedback. As I am very new to these methods and approach (I am starting an MSc) I would appreciate any advice you have as I'm finding my dataset quite complicated for the model selection approach I was advised to do.